class_docstr: Softmax activation layer.
  
  Formula:
  ``` python
  exp_x = exp(x - max(x))
  f(x) = exp_x / sum(exp_x)
  ```
  
  Example:
  >>>softmax_layer = keras.layers.activations.Softmax()
  >>>input = np.array([1.0, 2.0, 1.0])
  >>>result = softmax_layer(input)
  [0.21194157, 0.5761169, 0.21194157]
  
  
  Args:
      axis: Integer, or list of Integers, axis along which the softmax
          normalization is applied.
      **kwargs: Base layer keyword arguments, such as `name` and `dtype`.
  
  Call arguments:
      inputs: The inputs (logits) to the softmax layer.
      mask: A boolean mask of the same shape as `inputs`. The mask
          specifies 1 to keep and 0 to mask. Defaults to `None`.
  
  Returns:
      Softmaxed output with the same shape as `inputs`.
code: def get_config(self):
      config = super().get_config()
      config.update({"axis": self.axis})
      return config
  
file_path: keras/keras/layers/activations/softmax.py
imports: from keras import activations
  from keras import backend
  from keras.api_export import keras_export
  from keras.layers.layer import Layer
label_desc: Use image node_id 3 for calling the Softmax obj's underlying member method code with example usage: obj.get_config() and returns: config